Long-Term Efficacy in MRI and No Evidence of Disease Activity Outcomes in Patients With Relapsing-Remitting Multiple Sclerosis Treated With Peginterferon Beta-1a (P7.266)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the long-term efficacy in MRI and overall no evidence of disease activity (NEDA) outcomes of peginterferon beta-1a (PEG-IFN) in patients with relapsing-remitting multiple sclerosis (RRMS). BACKGROUND: ATTAIN is an extension study of the 2-year ADVANCE phase 3 study for patients with RRMS. The following analyses evaluated MRI and NEDA endpoints over 3 years of PEG-IFN treatment (ADVANCE Years 1, 2, and ATTAIN Year 1). DESIGN/METHODS: RRMS patients aged 18-65 received PEG-IFN dosed every 2 or 4 weeks throughout ADVANCE Years 1 and 2 and ATTAIN Year 1. Mean number of new T1 lesions, new/newly enlarging T2 lesions, and gadolinium-enhanced lesions (Gd+) were evaluated. Proportion of patients experiencing overall (clinical and MRI) NEDA over 3 years was also evaluated. RESULTS: In total, 376 patients receiving PEG-IFN every 2 weeks and 354 patients receiving PEG-IFN every 4 weeks from the ATTAIN ITT population were included in the analysis. At Year 3 (week 144), patients treated every 2 weeks and every 4 weeks, respectively, developed a mean of 0.2 and 0.8 Gd+ lesions, a mean of 3.2 and 5.5 new T1 lesions, and 7.6 and 16.3 new/newly enlarging T2 lesions when compared with baseline (day 0). The proportion of overall NEDA patients over 3 years using the ATTAIN ITT population was 32.4[percnt] and 20.1[percnt] for every 2 weeks and every 4 weeks, respectively (p=0.0002). NEDA results from the ADVANCE ITT population were similar; the proportion of overall NEDA patients over 3 years using the ADVANCE ITT population was 30.5[percnt] and 19.8[percnt] for every 2 weeks and every 4 weeks, respectively (p=0.0001). CONCLUSIONS: Patients with RRMS administered PEG-IFN dosed every 2 weeks displayed a reduction in MRI lesions and a significant increase in NEDA over 3 years of treatment compared with patients dosed every 4 weeks. Study sponsored by: Biogen Idec Inc. (Cambridge, MA, USA).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".